Camera trap data suggest uneven predation risk across vegetation types in a mixed farmland landscape

Abstract Ground‐nesting farmland birds such as the grey partridge (Perdix perdix) have been rapidly declining due to a combination of habitat loss, food shortage, and predation. Predator activity is the least understood factor, especially its modulation by landscape composition and complexity. An important question is whether agri‐environment schemes such as flower strips are potentially useful for reducing predation risk, for example, from red fox (Vulpes vulpes). We employed 120 camera traps for two summers in an agricultural landscape in Central Germany to record predator activity (i.e., the number of predator captures) as a proxy for predation risk and used generalized linear mixed models (GLMMs) to investigate how the surrounding landscape affects predator activity in different vegetation types (flower strips, hedges, field margins, winter cereal, and rapeseed fields). Additionally, we used 48 cameras to study the distribution of predator captures within flower strips. Vegetation type was the most important factor determining the number of predator captures and capture rates in flower strips were lower than in hedges or field margins. Red fox capture rates were the highest of all predators in every vegetation type, confirming their importance as a predator for ground‐nesting birds. The number of fox captures increased with woodland area and decreased with structural richness and distance to settlements. In flower strips, capture rates in the center were approximately 9 times lower than at the edge. We conclude that the optimal landscape for ground‐nesting farmland birds seems to be open farmland with broad extensive vegetation elements and a high structural richness. Broad flower blocks provide valuable, comparatively safe nesting habitats, and the predation risk can further be minimized by placing them away from woods and settlements. Our results suggest that adequate landscape management may reduce predation pressure.


| INTRODUC TI ON
Agricultural landscapes cover large areas (e.g., 45% in the EU, 46% in the USA [Bigelow & Borchers, 2017;EC, 2018]) and harbor an important part of terrestrial biodiversity (Krebs et al., 1999;Robinson et al., 2001). In the last decades, agro-biodiversity has been decreasing rapidly and many farmland bird species have exhibited drastic population declines (Burns et al., 2021;Kamp et al., 2021). Negative effects of agricultural intensification are the main drivers of these declines, in particular habitat loss due to an increase in field sizes and monocultures and food scarcity due to the increasing usage of pesticides and fertilizers (Donald et al., 2001(Donald et al., , 2006Gibbons et al., 2015;Newton, 2004;Pickett & Siriwardena, 2011). For example, the pesticide-induced lack of insects increases the mortality of grey partridge Perdix perdix chicks, which depend on insect-food in their first 2 weeks of life (Potts & Aebischer, 1995).
Corvids usually predate eggs or small chicks, while foxes and other mammals frequently prey on adult birds as well, in particular on incubating hens (Bro et al., 2000;Draycott et al., 2008;Gottschalk & Beeke, 2014;Potts, 2012). Hence, mammalian predators likely have a higher negative impact on ground-nesting farmland bird populations than avian predators.
Predator numbers in Europe have been increasing in recent decades following the successful anti-rabies vaccination of foxes and badgers Meles meles, decreasing hunting pressure, and the expansion of new predator species such as racoon Procyon lotor and racoon dog Nyctereutes procyonoides (Bartoszewicz, 2011;Beltrán-Beck et al., 2012;Chautan et al., 2000;Griffiths & Thomas, 1993;Kauhala & Kowalczyk, 2011;Keuling et al., 2011;Kowalczyk, 2014). However, increasing predator numbers account only partly for the increase in predation pressure. Changes in land use and landscape composition due to agricultural intensification also play a key role (Evans, 2004;Whittingham & Evans, 2004). Habitat loss can cause birds to nest in sub-optimal, exposed sites or to congregate in the few remaining habitat patches, which also are highly attractive for predators (Evans, 2004;Panek & Kamieniarz, 2000;Whittingham & Evans, 2004). Bad habitat conditions can further limit the possibility to compensate predation losses by rearing additional broods (Whittingham & Evans, 2004).
A study in France found that impoverished landscapes can drive partridges into riskier areas, for example in close proximity to woods, settlements, and roads (Harmange et al., 2019). In Poland, predation rates of grey partridges by foxes were higher in homogenous landscapes than in richly structured landscapes (Panek, 2013). In that study, fox activity in homogenous landscapes was concentrated in scarce permanent vegetation, which was also the preferred nesting habitat of partridges. In heterogeneous landscapes with a high number of hedges and other permanent vegetation, fox activity was distributed among a larger area and thus the encounter probability between partridges and foxes was lower (Panek, 2013).
Ongoing population declines in many ground-nesting farmland birds demonstrate that current conservation measures are not sufficient to maintain populations (Fox, 2004;Heldbjerg et al., 2018).
While habitat loss and food scarcity can be, at least partly, compensated by dedicated set-asides, flower strips, and other habitat improvements (Gottschalk & Beeke, 2014;Potts, 2012;Rands, 1986), high predation pressure remains a problem and may prevent population growth (Newton, 1998;Roos et al., 2018). Even predator presence alone (i.e., without a predation attempt) can cause disturbances and can have sublethal effects on ground-nesting birds (Cresswell, 2008;Cresswell & Quinn, 2013).
Habitat management may offer an alternative approach (Laidlaw et al., 2015(Laidlaw et al., , 2017. If we understand how predators use the landscape and where their activity, and thus the predation risk, is highest, we may be able to manage the landscape in a way that improves habitat quality and minimizes predation risk (Doherty & Ritchie, 2017;Evans, 2004;Laidlaw et al., 2021;Langgemach & Bellebaum, 2005;Roos et al., 2018).
At present, there are many open questions regarding the effect of landscape composition on predator activity and its implications for farmland bird conservation. How do landscape features such as forests, settlements, and water bodies influence predator activity? Can narrow, linear structures act as ecological traps (Eglington et al., 2009;Rantanen et al., 2010;Suvorov & Svobodová, 2012)?
Are landscapes with a lot of hedgerows more risky for groundnesting birds? Or do more structures lead to a better distribution of predator activity and thus decrease predation risk?
In this study, we investigate how predation risk by mammals is mediated by landscape composition. Grey partridges were the conservation target of this study, but the results could be equally valuable for other ground-nesting farmland birds and many species affected by high predation rates.
We ask (i) Which are the main predators in farmland? (ii) Are there differences in predator activity between vegetation types? (iii) Which environmental parameters explain spatial variation in predator activity best? And (iv) How do predators use flower strips, one of the most popular farmland conservation measures? 2 | ME THODS 2.1 | Data collection 2.1.1 | Study area The study area was located near Göttingen in Lower Saxony, Germany, and was based on the area covered by already existing partridge telemetry data to encompass the main partridge distribution in the district ( Figure 1). One part of the study area, "Diemarden," lay directly south of Göttingen and covered 35 km 2 . The other part, "Eichsfeld," was lo- forests were excluded from the study area, therefore forest cover is only 1.9% in "Diemarden" and 3.6% in "Eichsfeld," although both areas are bordered by extensive forests.

| Predator activity as a proxy for predation risk
We used predator activity as a proxy for predation risk because the predation risk posed by different predators for ground-nesting birds is difficult to measure directly. Activity was measured as the number of predator captures at each camera site. We assumed that a higher predator activity corresponded with a higher predation risk.

| Vegetation types
We focused on five vegetation types that were found to be important to grey partridges in spring and summer according to telemetry studies by Gottschalk and Beeke (2014): flower strips, field margins, hedges, winter cereal fields, and rapeseed fields. All flower strips in this study were "structurally rich flower strips," where one half of each flower strip is resown every year to create a mix of annual and perennial vegetation ("strukturreiche Blühstreifen" AUM BS12, Nds. Ministerium für Ernährung, Landwirtschaft und Verbraucherschutz, 2022). Flower strips were variable in width, from a minimum width of 6 m to extensive flowering areas. Field margins were grassy margins along the edge of fields. All hedges had a minimum length of 10 m and were at least 3 m wide.

| Camera traps
Browning Strike Pro HD camera traps (HDPX-5, Browning Trail Cameras) were used to record predators. They were mounted on wooden stakes approximately 40 cm above the ground and placed either in the center of the field or flower strip, or, for the vegetation type "field margin," on the border between field and field margin. In hedges, cameras were placed inside of the hedge wherever possible and next to the hedge otherwise. No bait was used, but cameras were placed along tractor lanes or animal paths to ensure a similar field of view. Cameras were set to take two sequential pictures once triggered to facilitate species identification.
F I G U R E 1 Map of the study area with the villages Diemarden and Nesselröden (CartoDB, 2021;NordNordWest, 2008;QGIS Development Team, 2021) 2.1.5 | Sampling design

Predator activity within the landscape
In the main survey, we used 120 camera stations that were evenly stratified between the five vegetation types (i.e., 24 cameras were placed in each vegetation type). The number of camera stations allocated to each of the two study areas was proportional to the available amount of each vegetation type. The camera sites themselves were distributed randomly. For this purpose, a 500 m × 500 m grid was overlaid over each study area and the grid cells for each vegetation type were chosen randomly. Only grid cells that were at least 50% inside the study area and had a maximum of 50% forest or settlement cover were considered and only one camera was allowed per grid cell.
Within a grid cell, we selected the available field (flower strip, hedge, field margin) that was closest to the center of the grid cell. Permission to install cameras was obtained from each farmer and game tenant.
Data sampling took place in 2019 and 2020 between May and July to align with the breeding season of grey partridges. Camera sites remained the same between years, except where winter cereal, rapeseed, or flower strip sites had to be changed due to crop rotation.
In these cases, the nearest suitable and available field was selected as replacement. Due to logistical constraints, only 40 sites could be sampled simultaneously. Therefore, we created three time blocks and cameras were rotated after each time block. In each time block, eight sites were chosen at random for each vegetation type. Cameras were in operation for at least 20 full days (max. 27 days). Cameras with less than 15 continuous sampling days were repeated once, either in the next time block or in a fourth time block at the end of the season. We only analyzed data collected during the longer sampling period.

Predator activity in flower strips
We complemented our main survey by studying, how predation risk is distributed in flower strips, namely, the differences between the edges and the interior of flower strips. Twenty-four randomly selected flower strips were sampled in August 2020, 12 in each part of the study area.
The flower strips were located around the villages of Diemarden and Nesselröden, respectively (see Figure 1). These areas were part of the Interreg Partridge Project (PARTRIDGE, 2022) and were chosen for easy access to the flower strips. In each flower strip, two cameras were placed simultaneously, one at the edge and one directly opposite 10 m inside of the flower strip. The inside camera was placed 10 m from the edge regardless of vegetation density, but an area of approximately 1 m 2 was cleared to allow visibility. The cameras at the edge had a larger field of view, but we included only predators that passed within 1 m of the camera in our analysis to ensure comparability across sampling locations. Cameras were in operation for 20-22 full days and they were checked once after 9-10 days to change SD-cards if necessary.

| Picture analysis
Pictures were sorted with Digikam 6.1.0 (digiKam, 2019) and all predators were identified to species level. Stone marten Martes foina and pine marten Martes martes were summarized as "marten" and domestic cats Felis catus and wildcats Felis silvestris were summarized as "cats," because identification to species level was not always possible. Wild boars Sus scrofa were considered predators for the purpose of this study as they frequently predate ground-nesting bird nests (Barrios-Garcia & Ballari, 2012). Consecutive records of the same species at the same site had to be at least 10 min apart to be considered independent captures, except when individuals could be identified. Multiple animals in the same picture were counted separately.

| Statistical analysis
All analysis were carried out using R version 4.1.3 (R Core Team, 2021) and figures were plotted using ggplot2 (Wickham, 2016) and ggeffects (Lüdecke, Aust, et al., 2021). Because our data were not normally distributed (Shapiro-Wilk Test, all p < .001, Table A1), non-parametric tests were used where applicable.
We combined data from both parts of the study area for our analyses. Several reasons motivated this choice: (a) both parts of the study area are very close together compared to their size and very similar in landscape composition, therefore we do not expect predator activity and predator's responses to environmental parameters to vary between areas, (b) we are interested in the effects of environmental predictors on predator activity, and those predictors should capture and explain any differences between the two areas, (c) a Wilcoxon rank sum test (R-package "stats", R Core Team, 2021) showed no significant differences between the activity indices of free-ranging predators (i.e., excluding dogs) in both areas (all p > .05, Table A2).
For completeness, the mean capture rate of domestic dogs Canis lupus familiaris is shown in Figure 2 (see Section 3). We excluded domestic dogs from all further analyses, however, because the number of dog captures depends on human behavior (e.g., popular walking routes or proximity to car parks) rather than the dog's habitat selection.

| Comparison of predator capture rates and vegetation types
To enable comparisons between sites with different sampling times, the number of observations per species was standardized as the capture rate per 100 camera days for each camera. To determine which predator species was the most prevalent, we compared capture rates between species for all camera sites and separately for each vegetation type.
Similarly, we compared capture rates between vegetation types.
To compare overall predator activity, we calculated the capture rate for all predator species except dogs together, hereafter "all predators," and compared that between vegetation types. We also compared fox capture rates between vegetation types, as foxes were revealed to be the most frequently observed predators (see Section 3).
Kruskal-Wallis rank sum tests (R-package "stats", R Core Team, 2021) were used for all comparisons and followed by Dunn's Post-Hoc tests with Holm's procedure to adjust p-values for multiple comparisons, if the former were significant (R-package "FSA" 0.9.2, Ogle et al., 2021). All comparisons were calculated based on the combined data for 2019 and 2020, because Wilcoxon rank sum tests (R-package "stats", R Core Team, 2021) found no significant differences between the years for any species or vegetation type (all p > .05, Table A3).

| Model set M1: Detailed models for predator and fox activity in summer
We used generalized linear mixed models (GLMMs) to analyze the effects of landscape composition and vegetation type on the number of total predator captures and fox captures separately. We focused on foxes in addition to "all predators" because they were by far the most prevalent predator species in our study (see Section 3) and are widely considered to be one of the most important predators for partridges and other ground-nesting birds (Langgemach & Bellebaum, 2005;Potts, 2012;Reynolds & Tapper, 1995;Roos et al., 2018).
For these models, we generated detailed landscape composition metrics within a buffer of 500 m around each cameras (see Section 2.3.2.1 below, Table 1). In addition, we performed a sensitivity analysis regarding the spatial scale at which predictors were measured by comparing three GLMMs based on predictors measured in 500 m, 1 km, and 2.5 km buffers around the camera sites, respectively.
The results confirmed that landscape composition at the local scale (500 m) was most important (see Appendix B for methods and results of this comparison; Tables B1-B6). Table 1 shows the predictors considered in the analysis of landscape composition effects on predator activity. All predictors were calculated in R 4.1.1 (R-package "sf" 1. We assessed the continuous environmental predictors for collinearity by calculating the Variance Inflation Factor (VIF) and sequentially dropped predictors with high VIF-values, until all VIF <3 ("HighstatLibV10.R" Zuur et al., 2009Zuur et al., , 2010. The area of arable land (Arable_Area) and road density (Road_Density) were dropped, because they were closely related to the area of woodland and distance to road (Wood_Area and Road_Dist), respectively. Furthermore, we dropped the mean field area (Mean_Field) as it was closely related to the length of field borders (Border_Length) and the area of field edges (Edge_Area) and we were more interested in the effect of field margin structure on predator activity. We assessed collinearity between the selected continuous predictors and the categorical predictor "vegetation type" by calculating the General Variance Inflation Factor (GVIF) and its derivative GVIF (1/2 df) , which corresponds to √VIF (Fox & Monette, 1992;"HighstatLibV10" Zuur et al., 2009). GVIF (1/2 df) was below 2 for all predictors (corresponding to a VIFvalue <4, Table A4), suggesting no collinearity in our remaining set of environmental predictors (compare Heringer et al., 2019;Min et al., 2019;Pebsworth et al., 2012;Vega et al., 2010).

Study covariates
We used a random effect of time block nested in year to account for variation in predator activity over time. Study site area (i.e., Diemarden or Eichsfeld) was not included as a covariate as there were no significant differences between "all predator" or fox activity between the areas (see Section 2.3). F I G U R E 2 Mean capture rates (captures/100 days) for each predator in all vegetation types. N sites = 240, 2019 and 2020 together. Kruskal-Wallis chi squared = 543.64, df = 8, p < .001 (Table A8). Letters correspond to significant differences following a posthoc Dunn's test (Table A9)

Model formulation
We analyzed predator activity by fitting GLMMs with a negative binomial distribution of errors and the number of captures as the response variable. Akaike's Information Criterion (AICc) corrected for small sample sizes was used for comparisons between models. Separate models were fit for "all predators" and "fox".
We used a negative binomial distribution, because GLMMs with a Poisson distribution indicated very strong overdispersion and a bad fit to the data. There was no zero-inflation detected and zeroinflated negative binomial models showed no improvement in model fit based on AICc. Models were fit using the R package glmmTMB Nakagawa's R 2 for mixed models (R-package "performance" 0.9.0, ).
Global models included distance to wood, distance to field edge, distance to water, distance to traffic, distance to settlement, wood area, extensive area, field margin, settlement area, water area, length of field borders, habitat diversity, and vegetation type as fixed effects and time block nested into year as random effect. In all models, flower strip was used as the reference level for the factorial covariate vegetation type. The runtime of each camera in minutes was used as offset to correct for sampling periods of different length.
We used backward selection based on AICc on the fixed effects to select the most parsimonious models. Starting with the global model, each fixed effect was dropped in turn and the AICc of the reduced model calculated. The fixed effect that caused the largest reduction in AICc was dropped permanently and the procedure repeated until no further reduction in AICc occurred. TA B L E 1 List of predictors considered in the analysis of predator and fox activity in model set 1 Note: Predictors in grey were not used in the full model due to collinearity issues. Vegetation types included in the Shannon Index were woods, water, settlements, field margins, winter cereal, summer cereal, fallow, maize, permanent grassland, winter rapeseed, summer rapeseed, orchards, turnips, short term woods, forage, root crops, protein crop, oilseed crops, pseudocereal, and "others." Data sources: B-DLM (LGLN, 2019; TLBG, 2019), InVeKos (SLA, 2019a, 2019b, 2020), our maps.

Relative variable importance
For each final model, we analyzed the relative importance of variables through a random permutation procedure. We randomized each variable in turn and calculated the correlation between the predictions made by the randomized and original models (Thuiller et al., 2009). This procedure was repeated 100 times for each variable. Next, we calculated the importance value for each variable as one minus the mean correlation between the predictions made by the original and randomized models and standardized the relative importance value to one (Thuiller et al., 2009).

| Predator and fox activity in and around flower strips
As before, the number of observations per species was standardized as the capture rate per 100 camera days to enable comparisons between sites with different sampling times. We used Wilcoxon signed rank tests with continuity correction (R-package "stats" , R Core Team, 2021) to compare fox and total predator capture rates between edge-cameras and inside-cameras in flower strips. All flower strips from Diemarden and Nesselröden were analyzed together, because a Wilcoxon rank sum test (R-package "stats", R Core Team, 2021) showed no significant differences between the capture rates of either "all predators" or foxes in both areas (Table A15). A Kruskal-Wallis test (R-package "stats", R Core Team, 2021) followed by a Dunn's Post-Hoc Test with Holm's procedure to adjust p-values for multiple comparisons (R-package "FSA" 0.9.2, Ogle et al., 2021) was used to compare capture rates between predator species at each position.

| RE SULTS
Overall, our main survey yielded 2122 camera trap observations of predators from 5024.697 active camera days over 2 years in summer 2019 and summer 2020. Over both years, depending on vegetation type, between 41.67% (in winter cereal) and 95.83% (in rapeseed) of all cameras recorded at least one predator (Table A6). In flower strips, 79.17% of the cameras recorded predators (Table A6). The following predators were captured: fox, racoon, badger, wild boar, marten, cats, stoat Mustela erminae, mouse weasel Mustela nivalis, and dogs.
In addition, we analyzed 236 predator observations from 855.409 active camera days recorded at the edge or in the center of flower strips in the second survey. Predators were recorded by 95.83% of all the cameras at the edge of flower strips and by 54.17% of the cameras in the center of flower strips. there was no significant difference compared to racoons (Table A9).   Figure 4). Figure 5 shows the mean capture rates of "all predators" and foxes in the center and at the edge of flower strips. For the edge capture rates, only predators that passed directly by the camera were included to avoid bias due to a larger field of view. In both cases, capture rates were very low in the center ( Figure 5; "all predators": mean 5.06, SD 6.05, fox: mean 2.45, SD 3.70; Tables A16 and A19) and

| Predator and fox capture rates within and at the edge of flower strips
significantly higher at the edge ( Figure 5; "all predators": mean 49.

| DISCUSS ION
Our study showed how risky farmland is for ground-nesting birds.
Of 240 cameras, 78.75% recorded at least one predator capture in 20 days. For comparison, grey partridges need around 40 days for laying and incubating a clutch (Cramp, 1980

F I G U R E 4
Plots of generalized linear mixed model "M1 fox activity" describing the effects of environmental parameters on the number of fox captures. Significant variables: Vegetation type, water area, field block borders (Table 2) F I G U R E 5 Mean capture rates (captures/100 days) of "all predators" and fox at the edge and in the center of flower strips. N Cameras = 24 at each position. Wilcoxon Signed Rank Test: "all predators": V = 13, p < .001, fox V = 15, p < .001 (Tables A16 and A19) Reynolds & Tapper, 1995;Roos et al., 2018). Fox activity appeared to be driven primarily by the vegetation type of the camera site, with wood cover, field borders, distance to settlements, and water surface area playing a smaller role.
The presumably "safest" places in farmland (i.e., those that had the least amount of predator captures) were winter cereal fields, whereas rapeseed fields had a high number of predator captures.
Rapeseed fields in summer provide good cover and can support high rodent populations (Heroldová et al., 2011), while the dense winter cereals may make prey less accessible and these fields less attractive to predators. However, in many areas partridges have a strong preference for permanent vegetation such as fallows, margins, and hedges as nesting habitat (Buner et al., 2005;Gottschalk & Beeke, 2014;Potts, 2012). Both the number of fox captures and total predator captures were lower in flower strips than in field margins or hedges, suggesting less predator activity and a lower predation risk in flower strips. This further supports the use of flower strips as highly effective conservation measures for ground-nesting farmland birds as they can provide safer nesting sites compared to other permanent vegetation structures. In contrast to mostly broad flower strips, hedges, and field margins form linear structures that many predators prefer for orientation, traveling, and hunting, which can explain the higher predator activity in these structures (Andrén, 1995;Bider, 1968;Bischof et al., 2019;Lidicker, 1999;Panek, 2013).
A closer look at predator activity in flower strips also revealed more than nine times as much predator activity along the edges than in the center, where only very few predators were captured. This suggests that predator activity within broad flower strips is much lower than in the surrounding area, presumably because the denser vegetation increases spatial resistance and many predators choose the easier path along the edge (Andrén, 1995;Bischof et al., 2019;Lidicker, 1999).
These findings corroborate results from Bro et al. (2000), who found higher predation rates of grey partridges in linear structures, and Gottschalk and Beeke (2014), who showed that nest losses of grey partridges in vegetation structures less than 10 m wide were twice as high as in broader vegetation structures. If the majority of predators move along the edges, the risk of detection and predation is higher in narrow structures and close to the edge. Thus, selection of microhabitats within one habitat type has a large impact on predation risk and the safety of flower strips depends on their shape and size. Broad flower blocks are important to provide safe nest sites.
We found that fox activity was lower in richly structured landscapes, as the number of fox captures was negatively related to field block border length as a measure for structural richness. The number of total predator captures showed a similar negative relation with field margin area (Table A13). Highly structured landscapes may have a lower predation risk due to a "dilution effect," whereby predators are more widely dispersed among available structures, decreasing the probability of encountering a predator at any given site. Additionally, a structurally rich landscape can offer more suitable nest sites and prevent birds from clustering together in unsuitable or isolated vegetation patches, thereby further reducing predation risk. Similar explanations for this pattern have been proposed by others, for example, Evans (2004) and Whittingham and Evans (2004). Our results also align with those of Panek (2013) who found a higher encounter probability of partridges and foxes in homogenous landscapes with few hedges compared to heterogeneous landscapes. Similarly, Kuehl and Clark (2002) found that the length of strip habitat (i.e., road ditches and fences) was negatively related to the presence of foxes and raccoons. The "all predator model" further showed a positive effect of habitat diversity (Table A13), suggesting that increasing habitat diversity can increase predator activity and thereby predation risk. This is likely due to diverse landscapes supporting larger and more diverse predator communities (Pita et al., 2009;Tews et al., 2004). Yet, our results indicate that this effect may be at least partially mitigated by highly structured landscapes with a large amount of edge structures, which have been shown to reduce the encounter probability between predator and prey. The Shannon Index that we used to measure habitat diversity cannot differentiate between different field sizes and landscapes with the same Shannon Index value could still be widely different in their structure. Additionally, the final fox model did not include habitat diversity, which further indicates that predation risk is affected more by landscape structure than habitat diversity.
We found wood cover to be positively related to fox captures, similar to previous studies (Jankowiak et al., 2008;Keuling et al., 2011;Kuehl & Clark, 2002;Weber & Meia, 1996). Hedges, woods, and forests can be highly attractive for many predators, as they provide cover, den sites, and a variety of different food resources (e.g., small mammals, bird nests, fruit) throughout the year (Janko et al., 2012; Keuling et al., 2011;Michel et al., 2007). Consequently, wood-rich landscapes may support high fox numbers and increase fox activity in the surrounding areas.
Villages with surrounding gardens and small scale livestock and poultry farming, as in our study area, provide a variety of food sources for foxes, which could explain why the number of fox captures was higher closer to settlements (Janko et al., 2012;Jankowiak et al., 2008).
Consequently, if villages attract foxes, predation risk by foxes is likely to decrease with increasing distance from settlements.
Interestingly, water surface area had a negative relationship with fox captures, in contrast to previous studies that showed some preference for water-related habitats in foxes (Fiderer et al., 2019;Kuehl & Clark, 2002;Matos et al., 2009). In our study area, lakes and streams were generally surrounded by reed beds, hedges, and woods. This high availability of attractive vegetation structures may have led to a dilution effect, where predator activity near water was higher, but predators were more dispersed and less likely to pass the camera station.
These results suggest that the optimal landscape to reduce predation risk for ground-nesting farmland birds would be open farmland with small field sizes and many edge structures, but little to no woods and settlements. Interestingly, several studies came to similar conclusions regarding the ideal landscape for farmland birds. Guerrero et al. (2012) concluded that farmland bird densities in several European countries were higher in landscapes dominated by agriculture with small fields and a high crop diversity. A recent crossborder study in Austria and the Czech Republic also found a positive association between farmland bird abundance and diversity and habitat heterogeneity (Šálek et al., 2021). In Finland, field edge density had strong positive effects on farmland bird assemblages and seemed to be even more important than crop diversity, grassland, or fallows (Ekroos et al., 2019). These results are usually explained by a lack of nesting habitats and food resources in high intensity farmland compared to fallows, field margins, grasslands, and diverse crops (Ekroos et al., 2019;Guerrero et al., 2012;Šálek et al., 2021). Our results, however, suggest that predator activity may also play a role. If predator activity is lower or less dense in a landscape optimal for ground-nesting farmland birds, we would expect lower predation rates and higher breeding success, and therefore higher bird densities.

| CON CLUS ION
By looking at the landscape from a (mammalian) predators' point of view, we can distinguish between intensively used areas and those with less predator activity that are consequently safer for groundnesting birds. Understanding what factors affect the distribution of predator activity allows us to adapt management plans to mitigate predation risk and improve nesting success.
In summary, our study shows that predator activity depended primarily on vegetation type and additionally on wood cover, landscape structure, distance to settlements, and habitat diversity. for ground-nesting birds should ideally be placed in areas with little wood cover and away from settlements wherever possible, because woods support high numbers of predators and settlements are attractive for generalist predators, leading to higher predator activity and higher predation risk close to these features. Third, highly structured landscapes seem to decrease predation risk by reducing the encounter probability between birds and predators. Therefore, small-scale structures such as field margins, ditches, and fallows should be preserved and the use of small field sizes encouraged.
The optimal landscape for ground-nesting farmland birds seems to be open farmland with small fields, many edge structures, and broad flower blocks or similar areas as breeding habitat.

ACK N OWLED G M ENTS
We would like to thank all farmers and game tenants who provided access to their land to install camera traps. We thank L. Dumpe for providing contact to farmers and game tenants, and W. Beeke and L. Dumpe for providing habitat maps for part of the study area. We

CO N FLI C T O F I NTE R E S T
The authors declare that there is no conflict of interests.

DATA AVA I L A B I L I T Y S TAT E M E N T
All datasets and R scripts used in this study are available at the Dryad  (2)  TA B L E A 1 6 Mean capture rates (captures/100 camera days) of all predators in the centre and at the edge of flower strips "all predators" includes all predator species except dogs. "-" marks species not found in the respective vegetation type. N Cameras = 24 at each position. At edge cameras, only predators that passed within 1m of the camera were included. Additionally, capture rates and observations of all predators at the edge regardless of the distance to the camera are given below. SD = standard deviation, CI = confidence interval A PPE N D I X B

B .1 | M O D E L S E T M2 : CO M PA R I S O N O F PR E DATO R A N D FOX AC TI V IT Y AT D I FFE R E NT S C A LE S
To investigate how the effects of landscape composition on predator activity differ on different scales, we constructed three different GLMMs based on predictors measured in 500 m, 1 km and 2.5 km buffers around the camera sites. We used only the main land use types as predictors for these models, because detailed data of small vegetation structures was not available on larger scales.  (Table B2). Time block nested in year was included as a random effect to account for temporal variation in predator activity.

VA R I A B LE I M P O RTA N CE
Separate models were fit for "all predators" and "fox" as response variables using the same procedure as described for model set